Using cohorts to infer attributes for an input case in a question answering system

ABSTRACT

A cohort analysis mechanism analyzes cohorts to infer one or more additional attributes for an input case to provide a refined input case to answer a question in a question answering system. The refined input case is then used to answer a question in the question answering system. The refined input case can be used in a traditional question answering flow or in a flow that again uses cohort analysis to extract relevant data to answer the question. The cohort analysis mechanism analyzes cohorts to find common attributes in the cohorts and then determines whether to infer the common attributes into the refined input case. The cohort analysis mechanism may determine to dialog with a user to confirm an inferred attribute.

BACKGROUND

1. Technical Field

This invention generally relates to computer question answering systems,and more specifically relates to using cohorts to infer additionalattributes for an input case to provide a refined input case in thequestion answering system.

2. Background Art

A significant purpose for computer systems is the retrieval of relevantinformation or documents from a store of knowledge. The typicalinformation retrieval system provides a document or file in response toa specific query or link. Question Answering (QA) is a specific type ofinformation retrieval that deals with returning information in responseto a natural language question. A QA response attempts to return aspecific answer such as a word or phrase to a question such as “who”,“where” or “what”. One example of a QA system is the Deep QuestionAnswering system, called “Watson”, developed by International BusinessMachines (IBM) Corporation of Armonk, N.Y. A user may submit a naturallanguage question (also referred to as a case) to Watson, which willthen provide an answer to the question based on an analysis of a corpusof information.

A QA system like Watson has application in the medical field due to theability to process and relate large amounts of information. For example,QA can determine an appropriate cancer treatment for a patient based onthe patient's medical history from knowledge stored in the database.While QA can identify knowledge stored in a large corpus using naturallanguage processing to interpret the English language, it is notdesigned to provide an answer when knowledge is non-existent, such aswhen the corpus does not contain sufficient knowledge to answer thequestion. When a question is posed about a topic that is not availablein a corpus, typically QA is at a loss to confidently answer thequestion.

BRIEF SUMMARY

A cohort analysis mechanism analyzes cohorts to infer additionalattributes for an input case to provide a refined input case to answer aquestion in a question answering system. The refined input case is thenused to answer a question in a question answering system. The refinedinput case can be used in a traditional question answering flow or in aflow that again uses cohort analysis to extract relevant data to answerthe question. The cohort analysis mechanism analyzes cohorts to findcommon attributes in the cohorts and then determines whether to inferthe common attributes into the refined input case. The cohort analysismechanism may determine to dialog with a user to confirm an inferredattribute.

The foregoing and other features and advantages of the invention will beapparent from the following more particular description of preferredembodiments of the invention, as illustrated in the accompanyingdrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The preferred embodiments of the present invention will hereinafter bedescribed in conjunction with the appended drawings, where likedesignations denote like elements, and:

FIG. 1 illustrates a block diagram of a computer system apparatus with aquestion answering application having a cohort analysis mechanism asdescribed herein;

FIG. 2 illustrates a block diagram of a question answering system asknown in the prior art;

FIG. 3 illustrates a block diagram of a question answering system usinga cohort analysis mechanism as described herein to infer an attribute;

FIG. 4 illustrates a block diagram of an example using a cohort analysismechanism as described herein to infer attributes;

FIG. 5 is a flow diagram of a method for analyzing cohorts to infer anattribute to answer a question in a question answering system;

FIG. 6 is a flow diagram of an example method for step 520 in FIG. 5 forusing cohort analysis to infer attributes for an input case in aquestion answering system; and

FIG. 7 is an example flow diagram to answer a medical question usingcohort analysis.

DETAILED DESCRIPTION

The disclosure and claims herein relate to a cohort analysis mechanismthat analyzes cohorts to infer additional attributes for an input caseto provide a refined input case. The refined input case is then used toanswer a question in a question answering system. The refined input casecan be used in a traditional question answering flow or in a flow thatagain uses cohort analysis to extract relevant data to answer thequestion. The cohort analysis mechanism analyzes cohorts to find commonattributes in the cohorts and then determines whether to infer thecommon attributes into the refined input case.

Referring now to FIG. 1, a computer system 100 is one suitableimplementation of an apparatus in accordance with the preferredembodiments of the invention. Computer system 100 represents a computersystem such as a Power System by International Business MachinesCorporation (IBM). However, those skilled in the art will appreciatethat the mechanisms and apparatus of the present invention apply equallyto any computer system, regardless of whether the computer system is acomplicated multi-user computing apparatus, a single user workstation,or an embedded control system. As shown in FIG. 1, computer system 100comprises a processor 110, a main memory 120, a mass storage interface130, a display interface 140, and a network interface 150. These systemcomponents are interconnected through the use of a system bus 160. Massstorage interface 130 is used to connect mass storage devices such as adirect access storage device (DASD) 155 to computer system 100. Onespecific type of direct access storage device 155 is a readable andwritable CD RW drive, which may store data to and read data from a CD RW195. Alternatively, the DASD may be a storage device such as a magneticdisk drive or a solid state disk drive.

Main memory 120 in accordance with the preferred embodiments containsdata 121, and an operating system 122. Data 121 represents any data thatserves as input to or output from any program in computer system 100.Operating system 122 represents an appropriate multitasking operatingsystem known in the industry such as “IBM i”, AIX (Advanced InteractiveeXecutive) or Linux; however, those skilled in the art will appreciatethat the spirit and scope of the present invention is not limited to anyone operating system. The main memory 120 also includes a QuestionAnswering (QA) application 123 that includes a cohort analysis mechanism(CAM) 124. The CAM finds and analyzes cohorts 126 to infer attributes togenerate a refined input case 127 as described further below.

Computer system 100 utilizes well known virtual addressing mechanismsthat allow the programs of computer system 100 to behave as if they onlyhave access to a large, single storage entity instead of access tomultiple, smaller storage entities such as main memory 120 and DASDdevice 155. Therefore, while data 121, operating system 122, QAapplication 123, the CAM 124, questions 125, cohorts 126 and the refinedinput case 127 are shown to reside in main memory 120, those skilled inthe art will recognize that these items are not necessarily allcompletely contained in main memory 120 at the same time. It should alsobe noted that the term “memory” is used herein to generically refer tothe entire virtual memory of computer system 100, and may include thevirtual memory of other computer systems coupled to computer system 100.

Processor 110 may be constructed from one or more microprocessors and/orintegrated circuits. Processor 110 executes program instructions storedin main memory 120. Main memory 120 stores programs and data thatprocessor 110 may access. When computer system 100 starts up, processor110 initially executes the program instructions that make up operatingsystem 122. Operating system 122 is a sophisticated program that managesthe resources of computer system 100. Some of these resources areprocessor 110, main memory 120, mass storage interface 130, displayinterface 140, network interface 150, and system bus 160.

Although computer system 100 is shown to contain only a single processorand a single system bus, those skilled in the art will appreciate thatthe cohort analysis mechanism may be practiced using a computer systemthat has multiple processors and/or multiple buses. In addition, theinterfaces that are used in the preferred embodiment each includeseparate, fully programmed microprocessors that are used to off-loadcompute-intensive processing from processor 110. However, those skilledin the art will appreciate that the present invention applies equally tocomputer systems that simply use I/O adapters to perform similarfunctions.

Display interface 140 is used to directly connect one or more displays165 to computer system 100. These displays 165, which may benon-intelligent (i.e., dumb) terminals or fully programmableworkstations, are used to allow system administrators and users tocommunicate with computer system 100. Note, however, that while displayinterface 140 is provided to support communication with one or moredisplays 165, computer system 100 does not necessarily require a display165, because all needed interaction with users and other processes mayoccur via network interface 150.

Network interface 150 is used to connect other computer systems and/orworkstations (e.g., 175 in FIG. 1) to computer system 100 across anetwork 170. The present invention applies equally no matter howcomputer system 100 may be connected to other computer systems and/orworkstations, regardless of whether the network connection 170 is madeusing present-day analog and/or digital techniques or via somenetworking mechanism of the future. In addition, many different networkprotocols can be used to implement a network. These protocols arespecialized computer programs that allow computers to communicate acrossnetwork 170. TCP/IP (Transmission Control Protocol/Internet Protocol) isan example of a suitable network protocol.

FIG. 2 illustrates a simplified block diagram of a question answeringsystem 200 as known in the prior art. In this example, the questionanswering (QA) system 200 is divided into a training pipeline 210 and aruntime pipeline 212. The training pipeline is used to train the systemand build machine learning (ML) models 214. The ML models 214 are thenused in the runtime pipeline 212. To train the QA system 200, questionsreferred to as input cases 216 are applied to a question analysis block218. During question analysis the system attempts to understand what thequestion is asking and performs the initial analyses that determine howthe question will be processed by the rest of the system. After questionanalysis, the system passes the question to the candidate generator 220.In the candidate generator 220, a search is performed to find as muchpotentially answer-bearing content as possible. Techniques appropriateto the kind of search results are applied to the search results togenerate candidate answers. Search results from the candidate generator220 are passed to the evidence retrieval block 222. To better evaluateeach candidate answer, the system gathers additional supportingevidence. This evidence is passed to the answer scoring block 224. Inthe answer scoring block 224 the bulk of the deep content analysis isperformed. Scoring algorithms determine the degree of certainty thatretrieved evidence supports the candidate answers. The QA system mayinclude many different components, or scorers, that consider differentdimensions of the evidence and produce a score that corresponds to howwell evidence supports a candidate answer for a given question. Afteranswer scoring is the final merger block 226. The goal of final mergingis to evaluate the hundreds of hypotheses based on potentially hundredsof thousands of scores to identify the single best-supported hypothesisgiven the evidence and to estimate its confidence, which is thelikelihood it is correct. After answer scoring the ML models arecreated. The ML models 214 assign weights to the system's variousanalysis programs according to how well they predict correct answers forthe case.

Again referring to FIG. 2, the question answering system 200 furtherincludes a runtime pipeline 212. The runtime pipeline is in many wayssimilar to the training pipeline 210. The runtime pipeline inputs cases228 to a question analysis block 230 and the question analysis blockfeeds a candidate generator block 232. Similar to the training pipeline,the runtime pipeline includes an evidence retrieval block 234, an answerscoring block 236 and a final merger block 238. The final merger block238 uses the ML models 214 created by the training pipeline 210. Theruntime pipeline 212 produces output cases 240.

FIG. 3 illustrates a block diagram representing an example of a questionanswering system 300 that utilizes cohort analysis as claimed herein.The question answering system 300 may also incorporate a trainingpipeline 210 as described in FIG. 2, however a training pipeline is notshown here for simplicity. The question answering system 300 includes aruntime pipeline 310 with input cases 125 to the pipeline in a similarmanner as the prior art. The runtime pipeline 310 includes blocks 316,318, 320, 322, 324 that are also similar to the respective blocks in theprior art discussed above. Question analysis block 316 may also besimilar to the prior art but may contain additional features asdescribed below. In addition the runtime pipeline 310 includes blocks326 and 328 as described further herein.

The runtime pipeline at some point determines whether to use cohortanalysis to infer attributes and generate a refined input case 127. Inthe illustrated example in FIG. 3, this determination to use cohortanalysis is done in block 326 by the CAM 124 (FIG. 1). Alternatively thedetermination to use cohort analysis could be part of block 316. Thedetermination to use cohort analysis to infer attributes may includeinput from a system administrator. The determination may also depend onthe type of question and the type of data available to the system. Forexample, cohort analysis may be attempted for questions of a specifictype where sufficient data is available. For example, data in the corpusmay be determined to be sufficient where there are cohorts in the corpuswith at least 90% of the attributes identified in the input case orwhere the most critical attributes are in the cohort regardless of thetotal percentage of attributes. If there is insufficient data cohortanalysis would be aborted. If there is sufficient data the case would bepresented to the cohort analysis mechanism (CAM) 124. The CAM 124 thenuses cohort analysis to infer attributes and generate a refined inputcase 127 as described herein.

The input cases 125 are applied to a question analysis block 316 andprocessed similar to the prior art. After question analysis, the CAM 124in block 326 determines whether to use cohort analysis to inferattributes from the question or input case. If the system determines notto use cohort analysis then it would proceed as known in the prior artshown in FIG. 2. If the system determines to use cohort analysis toinfer attributes in question analysis, the CAM infers attributes usingcohort analysis to provide a refined input case 127 as described furtherherein. The refined input case from the CAM is then passed to thecandidate generator 318 to search for candidates for the refined inputcase. Search results from the candidate generator 318 are passed to theevidence retrieval block 320. Retrieved evidence is passed to the answerscoring block 322. After evidence scoring is the final merger block 324.Blocks 318, 320, 322 and 324 may perform in a similar way to the priorart discussed with respect to FIG. 2. These blocks also work inconjunction with the cohort analysis mechanism to use cohort analysis toanswer a question as described herein.

In a previous application, filed on Sep. 17, 2014, Ser. No. 14/489,124by the same inventors as the instant application, a method was disclosedto use cohort analysis to answer a question in a question answeringsystem. This previously disclosed method can be used in conjunction withthe present invention which is represented by alternative flow 330 shownin FIG. 3. In the alternative flow 330, after generating a refined inputcase 127, the system may send the refined input case to block 328 andproceed to again use cohort analysis to answer the refined input case127 using the method of the previously filed application. In this methodin block 328, cohorts are first identified using the refined input case127. The system then extracts data from the cohorts, combines and ranksanswers from the cohorts, gathers evidence and then answers the questionwith the aggregated evidence as described in the previously filedapplication. After using cohort analysis to answer the question, theanswer can be sent to the answer scoring block 322.

The answer scoring block 322 can optionally use the answer from the CAM124 combined with answers using conventional QA analysis from evidenceretrieval block 320 for the same input case to answer the question.

As introduced above, the input case or question is analyzed to determineif cohort analysis can be used. Part of this analysis may includedetermining if the question deals with an entity that has availablecohorts. As used herein, a cohort is an entity for which there is datafor similar entities in the corpus of data available to the QA system.Entities for which cohort analysis could be applied may includepatients, people in general, animals, computer components, etc.Identifying cohorts entails finding entities in the corpus of dataavailable to the QA system that are similar to the one in the inputcase. The CAM matches attributes from the question or input case tosimilar entities available in the corpus of data using “fuzzy” matching.Fuzzy matching may be accomplished, for example, with a thresholdpercentage. Thus cohorts are similar entities that meet a threshold ofattributes similar to the input case where the threshold may be areference percentage. The cohorts may then be divided into relativestrengths such as “strong”, “medium” and “weak” for different percentagethresholds. The relative strength of the cohorts can then be used toscore the answers and evidence.

FIG. 4 illustrates a simplified example of cohort analysis by the CAM124 (FIG. 2) to infer attributes for an input case 410 to provide arefined case 412. The refined input case 412 can then be used to answera question in a question answering system as described herein. In thisexample, we assume the question is “What is the best treatment for a 54year old, white male with symptoms A, B and C?” The CAM determines thatthe question concerns a medical patient that has sufficient data in thecorpus of the data available to use cohort analysis. The first step isto identify cohorts that are similar to the one in the input case 410.The CAM identifies cohorts 414 for the patient of the input case 410.Cohorts 414 represent an example of cohorts 126 in FIG. 1. The cohorts414 include 6 patients that have similar age, race and symptoms as theinput case 410. In this example a small number of cohorts are shown forsimplicity. However, in actual use the number of cohorts wouldpreferably be much larger to get more accurate results. The CAM thenfinds missing attributes in the input case 410 by comparing attributesof the cohorts 414. In this example, the CAM identifies symptoms “D”,“E” and “F” as missing attributes from the input case compared to thecohorts.

Again referring to FIG. 4 after identifying the missing attributes, theCAM then scores the missing attributes. In this example the CAM scoresthe missing attributes as shown in block 416. In this example, the CAMfinds the number of patient cohorts that have the missing attributes(symptoms) of the input case. As illustrated, the CAM has identifiedpatients 3, 4 and 6 having the missing attribute or symptom D.Similarly, patient 6 was found to have the missing symptom E and patient2 was found to have the missing symptom F. The CAM would then score thesymptom D as having the highest consistency in the identified cohorts.The CAM then applies confidence thresholds to the scored attributes toinfer attributes for the input case. For example, the confidencethreshold may be determined by finding the percentage of cohorts thathave the missing attribute and comparing it to a predetermined thresholdor a threshold provided by a user. In this example, the CAM uses aconfidence threshold such that symptom D is inferred to the input caseto produce a refined input case 412 as shown that includes symptom D inthe patient data.

As described above with reference to FIG. 4, the CAM applies confidencethresholds to the scored attributes to infer attributes for the inputcase. In addition, the CAM may determine to dialog with a user todetermine whether to infer the attributes. In the above example, the CAMmay determine to dialog with the user to ask the user whether to infersymptom D. This determination to dialog with the user may be done whenthe confidence level for the inferred attribute does not meet aparticular threshold.

For example, if the confidence level is sufficiently high, for exampleabove 70%, then attribute would be inferred. If however the confidencelevel is lower, for example above 50% but below 70% the attribute willonly be inferred into the input case after dialoging with the user. Inthe above example, the user may be asked to answer a question such asthe following: “Symptom D appears to be a common symptom for patientshaving similar problems as the input case. Did the input case have ormay have had symptom D?” If the user affirms then symptom D is added tothe input case. The CAM could also score the attributes to infer anattribute for the input case based on the size of the cohorts. Forexample, if only 10 patients are in the cohort then the CAM mightrequire a 90% confidence to be sufficient to infer an attribute.However, with a larger sample size the CAM could accept a lowerconfidence threshold such as 70%.

After the CAM provides a refined input case, the refined input case canbe passed to the candidate generator 318 (FIG. 3). After this point inthe runtime pipeline shown in FIG. 3, the flow to answer the questioncan be essentially as done in the prior art. Alternatively, the refinedinput case 412 can be sent to block 328 (FIG. 3) and cohort analysis canbe used to answer the question in a manner previously disclosed. Usingcohort analysis to answer the question includes the steps of identifyingcohorts for the entity of the question, extracting data from the cohortsthat were identified, combining and ranking answers from the cohorts,gathering evidence and then answering the question with the aggregatedevidence. After using cohort analysis to answer the question, the answercan be sent to the answer scoring block 322. The answer scoring block322 can optionally use the answer from the CAM 124 combined with answersusing conventional QA analysis from evidence retrieval block 320 for thesame input case to answer the question. Extracting data from the cohortsmay include potential answers for the question and the associatedevidence. For example, common answers are combined and ranked accordingto occurrence in the data. The ranking can then be used to gather thestatistically most significant evidence to answer the question and givea confidence score for the answer. The answers can be scored by aconfidence in the answer. Confidence in cohort attributes is a directresult of statistical analysis of the cohesion of values for the cohort.The confidence for an answer is the statistical likelihood that the mostfrequent value for that attribute is correct given the size of thecohort and the number of cases in the cohort with the same value.

Referring now to FIG. 5, a flow diagram shows a method 500 for usingcohort analysis to infer attributes in a question answering system. Thesteps of method 500 are preferably performed by the cohort analysismechanism (CAM) 124 as part of a question answering system as describedabove. First analyze the input case or question (step 510). Next, usecohort analysis to infer attributes for the input case (step 520).Provide a refined input case with the inferred attributes (step 530).Answer the input case or question with the refined input case (step540). The method 500 is then done.

Referring now to FIG. 6, a flow diagram shows a method 600 for usingcohort analysis to infer attributes in a question answering system. Thesteps of method 600 are preferably performed by the cohort analysismechanism (CAM) 124 as part of a question answering system as describedabove. First identify cohorts of an entity of the input case or question(step 610). Next, analyze the identified cohorts to find commonattributes (step 620). Find missing attributes in the input case fromthe common attributes in the cohorts (step 630). Score the missingattributes according to consistency, sample size, etc. (step 640). Applyconfidence thresholds to the missing attributes and determine whether toinfer attributes to the input case (step 650). Alternatively, applyconfidence thresholds to the missing attributes and determine whether todialog with the user to confirm inferred attributes (step 660). Thenprovide a refined input case with the determined inferred attributes(step 670). The method 600 is then done.

Referring now to FIG. 7, a flow diagram shows a method 700 for usingcohort analysis to answer a question in a question answering system. Thesteps of method 700 are preferably performed by the cohort analysismechanism as described above. First process the medical history for apatient (step 710). Use cohorts and the steps of method 500 to performcohort analysis to infer an attribute for an input case (step 720). Thenpredict an answer to the input case based on the inferred attribute(step 730). The method is then done.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The disclosure and claims are directed to a cohort analysis mechanismthat uses cohort analysis to infer attributes for an input case in aquestion answering system.

One skilled in the art will appreciate that many variations are possiblewithin the scope of the claims. Thus, while the disclosure isparticularly shown and described above, it will be understood by thoseskilled in the art that these and other changes in form and details maybe made therein without departing from the spirit and scope of theclaims.

1. An apparatus comprising: at least one processor; a memory coupled tothe at least one processor; a cohort analysis mechanism residing in thememory and executed by the at least one processor that analyzes an inputcase representing a question and finds cohorts with similar attributesto the input case to infer an attribute for the input case, provides arefined input case that includes the inferred attribute, and answers thequestion using the refined input case; and wherein the cohorts areentities with similar characteristics to an entity of the input case andavailable in a corpus of data.
 2. The apparatus of claim 1 wherein thecohort analysis mechanism analyzes the cohorts to find common attributesin the cohorts and finds missing attributes in the case for the commonattributes in the cohorts.
 3. The apparatus of claim 2 wherein thecohort analysis mechanism scores the missing attributes.
 4. Theapparatus of claim 3 wherein the cohort analysis mechanism appliesconfidence thresholds to the missing attributes to determine whether toinfer the attribute for the refined input case.
 5. The apparatus ofclaim 4 wherein the cohort analysis mechanism determines whether todialog with a user to confirm the inferred attribute.
 6. The apparatusof claim 5 wherein the cohort analysis mechanism uses a lower thresholdto determine whether to dialog with the user compared to inferring theattribute without dialoging with the user.
 7. The apparatus of claim 1wherein the input case is a medical question for a patient and thecohort analysis mechanism processes the medical history of the patientand finds the cohorts from medical histories of other patients to inferan attribute for the input case from the cohorts.
 8. The apparatus ofclaim 1 wherein the cohort analysis mechanism is part of a questionanswering application that answers a natural language question.